Hi there,
I am analysing some RNA expression from an experiment with a set up like:
sample condition
R1 A
R2 A
R3 A
C1 B
C2 B
C3 B
I have compared RNA expression between conditions It is mean : (R1,R2,R3) vs (C1,C2,C3)
by doing:
library(DESeq2)
library(tidyverse)
#### Load data
library(readxl)
setwd("~/Documents/path/to/txt/file/")
data= read.table("Expression_level.txt", header = T)
View(data)
R1 R2 R3 C1 C2 C3
gene-CpipJ_CPIJ008101 484021 412077 445173 154707 148776 169263
gene-CpipJ_CPIJ001132 334997 391789 435968 445623 504466 445865
gene-CpipJ_CPIJ006209 326414 260289 301946 169859 149214 141446
gene-CpipJ_CPIJ002271 320207 282722 326901 203648 170398 134834
gene-CpipJ_CPIJ005941 316818 252593 273103 55266 43730 26304
gene-CpipJ_CPIJ009303 269236 357244 386633 426546 531801 483546
gene-CpipJ_CPIJ010326 233568 226659 254108 362953 278742 325969
gene-CpipJ_CPIJ008915 230936 276916 277624 355937 357974 239651
gene-CpipJ_CPIJ009571 223388 187980 207711 128457 139515 87437
annotation.info <- read.table("~/Documents//path/to/txt/file/",header = T)
Viewannotation.info)
sample condition
R1 A
R2 A
R3 A
C1 B
C2 B
C3 B
## Create Data Set
dds <- DESeqDataSetFromMatrix(countData = data,
colData = annotation.info,
design = ~ condition)
#do the analyses
dds <- DESeq(dds)
res <- results(dds)
res
## I keep only differentially expressed genes
subset(res,padj<0.05)->subset
summary(subset)
out of 7420 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 3751, 51%
LFC < 0 (down) : 3669, 49%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
BUT I want to know which of these up and down regulated genes belong to the condition A and B. I mean, how many genes are up and down regulated for each condition.
Is there any way of get this information?
Thank you so much